BAM! How to link your business analytics to your business goals.

Jumping on the big-data bandwagon without an empirically-based framework that links to business goals is perilous. Some recent research (Apr 2018) shows promise to assist guiding organisations in aligning their business analytics projects to their business goals.

There is so much hype around business analytics and big-data that it risks falling into the “fad” category. Organizations are rightly keen to explore how they can capture and use large volumes of data to create value and competitive advantage, but a recent study notes there is little evidence-based research in this area, and many organisations that jump on the big data analytics bandwagon, “are likely to waste money, resources, and attention in their quest to become data-driven and to adopt evidence-based decision making”.

I’m no expert in big-data, but I am an expert in aligning business and technology strategies, and one thing I know for sure, unless a proposed changed aligns with overall business needs, it is doomed to be of no value to the business.

Two leading data scientists, Giles Hindle and Richard Vidgen set out so explore how organisations can align their business analytics development projects with their business goals and strategy.  They propose a business analytics methodology (BAM) which shows promising application.

The BAM provides a logical set of steps that can be used to inform, prioritise, and guide the practice of business analytics. In some ways it shares similarities with standard business analysis practices: in other ways, it differs. Whilst not being particularly prescriptive, it proposes four distinct stages – problem-situation structuring, business model mapping, analytics leverage analysis, and implementation.

Problem-Situation Structuring

Problem-situation structuring is similar to a current state analysis: it explores the the context in which analytics will be deployed. Importantly, the as-is situation is expressed “in all its messiness”, capturing alternative viewpoints, incongruities, and chaotic elements as well as identifying key issues and features. The intent is to understand how the business model functions as a whole in reality, and to capture the genuine interests and worldviews of the various stakeholders.

Business Model Mapping

Once the business model is understood, business model mapping formally maps it. Business model mapping conceptualises the business as a “purposeful activity system” and can open up opportunities for business model innovation. It allows for more detailed analysis of the activities highlighted in the first stage, and also the generation of system-level performance measures.

Analytics Leverage Analysis

In this phase, the outputs from the previous stages are systematically mapped against potential analytics applications. An important point here is that business issues are framed as questions that the business needs answers to if it is to make effective and better decisions. Potential applications for analytics are then considered and categorised using a value creation matrix, that considers (a) the potential for business value creation, and (b) the degree of analytics difficulty. Applications that have a low degree of value, and a high degree of difficulty are called “hard slogs” and should not proceed; those that offer the most value creation for the lowest degree of difficulty are considered quick-wins and should proceed; and those that offer a high degree of value but involve a high degree of difficulty should be investigated with a more comprehensive business case analysis.

Analysis Implementation

The implementation phase undertakes further exploration at a data level: to understand in detail what data is current available, the quality of the existing data, and what extra data might be needed. Patterns in existing data are also analysed, as well as exploring potential sources of open data and enhanced data collection options. Further details of the implementation approach can be found in the original article, “Developing a business analytics methodology: a case study in the foodbank sector”, published in the European Journal of Operational Research, Apr 2018.[1]

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Whilst the findings from this study are limited to a single case study, it provides a promising framework for organisations considering jumping on the bigdata analytics bandwagon to keep focus on value-creation opportunities. It also emphasises the important role of data and analytics in both fitting with the business model, and supporting business goals.

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[1] Hindle, GA, & Vidgen R (2018). “Developing a business analytics methodology: A case study in the foodbank sector”. European Journal of Operational Research, Vol 268 (Apr 2018), pp 836-851, http://dx.doi.org/10.1016/j.ejor.2017.06.031


 

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